BIG Catalogue
imageryBaseMapsEarthCover
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The Paraiba State Mosaic was generated with technologies under development in the Brazil Data Cube project, using the best pixel (free of cloud and cloud shadows) for three months (April, May, and June 2020). This mosaic was generated using CBERS-4A (55 meters).
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The CBERS-4 WFI Brazil Mosaic covers the entire Brazilian territory. The mosaic uses surface reflectance images from the CBERS satellite, a WFI imaging camera (or sensor) with 64 meters of spatial resolution. It is a composition of images from April to June 2020, selecting the best pixel within the period. The final product is an RGB color composite with the red (B15), NIR (B16), and blue (B13) bands.
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Earth Observation Data Cube generated from CBERS-4/WFI and CBERS-4A/WFI Level-4 SR products over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 8 days using the Least Cloud Cover First (LCF) best pixel approach.
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This is a land cover classification map of Brazilian Caatinga, from January to December of 2017. This classification was made on top of Landsat-8 16 days data cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 9324 sample points of the classes Agriculture 172, Country formation 577, Forest (Formação florestal) 222, Savanna 4819, Pasture 3538. The spectral band used were B1, B2, B3, B4, B5, B6, B7, along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask algorithm and estimated using linear interpolation. The classification algorithm was Random Forest. The post-processing consisted on cropping the images to the biome's boundary. This product was funded by the Brazilian Development Bank (BNDES).
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This is a land cover classification map of Brazilian Mata Atlantica, from January to December of 2017. This classification was made on top of Landsat-8 days data cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 13442 sample points of the classes Agriculture 2668, Planted forest 823, Forest (Formação florestal) 3754, Pasture 6197. The spectral bands used were B1, B2, B3, B4, B5, B6, B7, along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask algorithm and estimated using linear interpolation. The classification algorithm was Random Forest. The post-processing consisted on cropping the images to the biome's boundary. This product was funded by the Brazilian Development Bank (BNDES).
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Earth Observation Data Cube generated from CBERS-4/MUX Level-4 SR product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 20 meters of spatial resolution, reprojected and cropped to BDC_MD grid Version 2 (BDC_MD V2), considering a temporal compositing function of 2 months using the Least Cloud Cover First (LCF) best pixel approach.
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Earth Observation Data Cube generated from CBERS-4/WFI Level-4 SR product over Brazil extension. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 64 meters of spatial resolution, reprojected and cropped to BDC_LG grid Version 2 (BDC_LG V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.
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Sentinel-2 image mosaic of Brazilian Yanomami Indigenous Territory with 10m of spatial resolution. The mosaic was prepared to support the partnership between the INPE's Health Information Investigation Laboratory (LiSS) and the ICIT-FIOCRUZ Health Information Laboratory (LIS) a multi-institutional body coordinated by Fiocruz and the ministry of health, by creating a health situation database of the Yanomami Indigenous Land. The false color composition is based on the MSI bands 11, 8A and 4 assigned to RGB channels. The temporal composition encompasses 06-months of images, starting in April 2019 and ending in September 2022, with a best pixel selection approach called Least Cloud Cover First (LCF). More information on LCF can be found at Brazil Data Cube web site (https://brazil-data-cube.github.io/specifications/processing-flow.html#temporal-compositing). This Image Mosaic used more than 15000 Sentinel-2 scenes and was generated based on an existing data cube of Sentinel-2 images.
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Earth Observation Data Cube generated from Copernicus Sentinel-2/MSI Level-2A product over Brazil. This dataset is provided in Cloud Optimized GeoTIFF (COG) file format. The dataset is processed with 10 meters of spatial resolution, reprojected and cropped to BDC_SM grid Version 2 (BDC_SM V2), considering a temporal compositing function of 16 days using the Least Cloud Cover First (LCF) best pixel approach.
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Os dados de avisos de desmatamento exibidos na aplicação AMS - Amazon Situation Room, são provenientes da base oficial do projeto DETER-B e incluem alguns atributos extras obtidos de processos de agregação de polígonos de desmatamento, chamado de cluster de desmatamento, atributos obtidos do cruzamento com dados do CAR - Cadastro Ambiental Rural e atributos obtidos do cruzamento com dados do CNFP - Cadastro Nacional de Florestas Públicas. Detalhes sobre os dados DETER-B de entrada: http://terrabrasilis.dpi.inpe.br/geonetwork/srv/eng/catalog.search#/metadata/f2153c4a-915b-48a6-8658-963bdce7366c CAR - Baixado de https://car.gov.br/publico/ em agosto/2019 CNFP - Baixado de https://www.florestal.gov.br/cadastro-nacional-de-florestas-publicas em julho/2021 Descrição das colunas padrão do DETER-B publicação oficial --------------------------------------------------------------------------------------------------------------------------- classname: Nome das classes atribuídas aos avisos, podendo ser: para degradação: ('CICATRIZ_DE_QUEIMADA', 'CS_DESORDENADO', 'CS_GEOMETRICO', 'DEGRADACAO') e para desmatamento ('DESMATAMENTO_CR', 'DESMATAMENTO_VEG', 'MINERACAO'); quadrant: Atualmente fora de uso para as imagens CBERS. No passado foi utilizada como parte da informação das imagens AWFI; path_row: Path e Row (orbita ponto) das imagens usadas na identificação do aviso; view_date: Data das imagens usadas na identificação do aviso; sensor: Nome do sensor embarcado no satélite, usado na obtenção da imagem; satellite: Nome do satélite que obteve a imagem; areatotalkm: Área calculada antes da fragmentação por intersecção. Não deve ser somada. Usada apenas para finalidade de filtro pela área original do aviso; areauckm: Área do aviso ou porção dele que intercepta uma unidade de conservação; uc: Nome da unidade de conservação interceptada pelo aviso; areamunkm: Área do aviso ou porção dele que intercepta um município. Use esta coluna em operações de soma de área; municipality: Nome do município interceptado pelo aviso ou porção dele; uf: Nome da Unidade da Federação na qual o aviso ou porção dele está localizado; Descrição das colunas de agregação --------------------------------------------------------------------------------------------------------------------------- ncar_ids: numero inteiro que indica quantas unidades do CAR fazem intersecção com o alerta; car_imovel: texto com lista dos identificadores de cada unidade do CAR que faz intersecção com o alerta; continuo: valor original é numero inteiro sendo 0 ou 1 e mapeado para (0=não e 1=sim) indicando se faz parte de um desmatamento continuo; velocidade: numero fracionário que indica a área desmatada em um cluster pelo numero de dias em que esta ativo. (hectares/dia); deltad: numero inteiro que indica há quantos dias o cluster está ativo; est_fund: dado textual com o nome curto proveniente de interseção com matriz de estrutura fundiária (ver tabela de domínio nomes_estrutura_fundiaria); dominio: dado textual que indica o nome da unidade no cadastro nacional de floresta publica; tp_dominio: dado textual que indica o tipo da unidade no cadastro nacional de floresta publica (ver tabela domínio tipo_cnfp); Cluster de desmatamento --------------------------------------------------------------------------------------------------------------------------- Calculado em memoria para representar um conjunto de desmatamentos próximos. Usa técnica de buffer com limiar de proximidade de até 60 metros entre os polígonos de desmatamento. tipo_cnfp: Tabela de tipos das unidades relacionados à forma de proteção --------------------------------------------------------------------------------------------------------------------------- "TIPO A" "USO MILITAR" "TIPO A" "PROTECAO INTEGRAL" "TIPO A" "OUTROS USOS" "TIPO A" "USO SUSTENTAVEL" "TIPO B" "SEM DESTINACAO" nomes_estrutura_fundiaria: Tabela de domínio com apelidos e nomes das estruturas fundiárias proveniente de compilado de dados do CAR(SFB/MAPA), Unidades de Conservação Federal e Estadual (ICMBio/MMA), Terras Indígenas(FUNAI/MJ) e Projeto Assentamento Rural (INCRA/MAPA): Fonte INPE/MCTI em agosto/2021 --------------------------------------------------------------------------------------------------------------------------- "BACKG" "Background" "TI" "Terra indigena" "UCE" "Unid. Cons. Estadual" "UCF" "Unid. Cons. Federal" "AC" "Área Consolidada" "APP_MR" "Área Preservação Permanente - margem rio" "AIMOV" "Área do Imóvel" "VNAT" "Vegetação Nativa" "RLA" "Reserva legal aprovada averbada" "RLP" "Reserva legal proposta" "APP_TM" "Área Preservação Permanente - topo morro" "POU" "Pousio" "UREST" "Uso Restrito" "QUIL" "Área Quilombola" "PAR" "Projeto Assentamento Rural" Nota sobre o SHAPEFILE: Ao exportar para shapefile os nomes das colunas sempre são reduzidos para dez (10) caracteres. Exemplo: a coluna "municipality" será renomeada para "municipali".